code: def __init__(
      self,
      img_size=384,
      patch_size=16,
      in_chans=3,
      class_num=1000,
      embed_dim=1024,
      depth=24,
      num_heads=16,
      mlp_ratio=4,
      qkv_bias=True,
      qk_scale=None,
      drop_rate=0,
      attn_drop_rate=0,
      drop_path_rate=0,
      norm_layer="nn.LayerNorm",
      epsilon=1e-6,
      **kwargs,
  ):
      super().__init__(
          img_size,
          patch_size,
          in_chans,
          class_num,
          embed_dim,
          depth,
          num_heads,
          mlp_ratio,
          qkv_bias,
          qk_scale,
          drop_rate,
          attn_drop_rate,
          drop_path_rate,
          norm_layer,
          epsilon,
          **kwargs,
      )
      self.patch_size = patch_size
      self.start_index = 1
      features = [256, 512, 1024, 1024]
      readout_oper = [
          ProjectReadout(embed_dim, self.start_index)
          for out_feat in features
      ]
      self.act_postprocess1 = nn.Sequential(
          readout_oper[0],
          Transpose(1, 2),
          Unflatten(2, [img_size // 16, img_size // 16]),
          nn.Conv2D(
              in_channels=embed_dim,
              out_channels=features[0],
              kernel_size=1,
              stride=1,
              padding=0,
          ),
          nn.Conv2DTranspose(
              in_channels=features[0],
              out_channels=features[0],
              kernel_size=4,
              stride=4,
              padding=0,
              dilation=1,
              groups=1,
          ),
      )
  
      self.act_postprocess2 = nn.Sequential(
          readout_oper[1],
          Transpose(1, 2),
          Unflatten(2, [img_size // 16, img_size // 16]),
          nn.Conv2D(
              in_channels=embed_dim,
              out_channels=features[1],
              kernel_size=1,
              stride=1,
              padding=0,
          ),
          nn.Conv2DTranspose(
              in_channels=features[1],
              out_channels=features[1],
              kernel_size=2,
              stride=2,
              padding=0,
              dilation=1,
              groups=1,
          ),
      )
  
      self.act_postprocess3 = nn.Sequential(
          readout_oper[2],
          Transpose(1, 2),
          Unflatten(2, [img_size // 16, img_size // 16]),
          nn.Conv2D(
              in_channels=embed_dim,
              out_channels=features[2],
              kernel_size=1,
              stride=1,
              padding=0,
          ),
      )
  
      self.act_postprocess4 = nn.Sequential(
          readout_oper[3],
          Transpose(1, 2),
          Unflatten(2, [img_size // 16, img_size // 16]),
          nn.Conv2D(
              in_channels=embed_dim,
              out_channels=features[3],
              kernel_size=1,
              stride=1,
              padding=0,
          ),
          nn.Conv2D(
              in_channels=features[3],
              out_channels=features[3],
              kernel_size=3,
              stride=2,
              padding=1,
          ),
      )
  
      self.norm = nn.Identity()
      self.head = nn.Identity()
  
imports: import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleclas.ppcls.arch.backbone.model_zoo.vision_transformer import VisionTransformer
label_desc: Use image node_id 1 to create a new ViT object from inherited base classes: VisionTransformer with example: obj = ViT(img_size, patch_size, in_chans, class_num, embed_dim, depth, num_heads, mlp_ratio, qkv_bias, qk_scale, drop_rate, attn_drop_rate, drop_path_rate, norm_layer, epsilon)